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The Ultimate Guide to Understanding LTV

Intro

If you’re one of many who are still focused on traditional KPIs, you’re likely overspending on people that will never convert and underinvesting in those who could be your best customers. Ouch.

Calculating lifetime value will help you to understand who your customers are and how to adjust your strategy accordingly to segment your current customers, make new customer acquisition more efficient, and increase your number of high value customers. We partnered with Google to lead the way by concentrating on customer value with a profit-driven marketing approach.

A Brief History of Digital KPIs

Digital marketing was built on clicks and CPC. A click was the necessary first step and the most important metric for a time. Yet clicks were actually just the means to an end: a conversion. And so as digital strategies and analysis matured, CPA became the top dog.

But again, conversions were really just the stepping stones to the next goal. And with increased ability to implement tracking, revenue became the focus. The picture of success was getting more refined, but was still a limited view. Prioritizing revenue was important, but it didn’t take into account the cost of doing business.

And this brings us to where we are today. The smartest marketers have learned how to leverage gross profit as the true best measure of success. They know who their best customers are by modeling lifetime value (LTV) and they use this information to inform all of their marketing decisions.

Shifting from Total Spend to LTV Strategy

Many companies group their customers by total spend. This can be a good first approach, but it has many flaws. For example, some of your historical top spenders may not have purchased anything in several years. What are the chances that these customers are still engaged with your brand?

Another approach is to try to add rules of thumb. “If a customer hasn’t purchased in the last 12 months, that person is considered churned.” While these types of rules are an improvement, they are not ideal since they don’t consider the statistical properties of customer transaction patterns. To address this issue, researchers develop statistical models to describe the transaction patterns of customers.

The model that has proven to be most efficient in our work is the customer LTV model.

The Profit-Driven Marketing Approach

Wpromote is closing the gap between analytics and real business success at the bottom line. The profit-driven approach is the way of the future. We’ve worked with Google to be on the forefront of this change in digital marketing and how it will affects every business.

Key idea: “By changing the way they invest based on the type of customers, brands can increase profit with less investment and increase the equity.”

Profit-driven marketing puts into numbers what everyone already knows: that some customers are worth more than others. It’s only when you know the difference in your customer groups and their value that you can optimize for profit. To make the changes you need, you have to calculate LTV.

Calculating LTV

The Fundamentals of LTV

One of the leading researchers on LTV is Peter Fader, a Professor of Marketing at Wharton Business School. Peter Fader has several research papers on this topic, and he co-founded Zodiac, a predictive analytics firm for customer valuation models and insights that has been acquired by Nike. But what is LTV? Well, let’s dig deeper.

One of the attractive features of LTV is that it requires a minimal amount of data. In fact, it only requires transaction log data, which is simply a dataset with the following columns:

  • Unique customer identifier
  • Transaction date
  • Value of each transaction

The beauty is that most companies already collect this information in one form or another. Here is an example:

RFM Analysis for Customer Segmentation

Using this data we can construct the “RFM object,” which summarizes each customer using 4 metrics from the transaction log. Here is a description of each:

  • Frequency: the number of repeated transactions, this is basically the total number of transactions the customer made minus 1. It measures how often a customer buys.
  • Recency: the number of months between the first and last transaction. It measures the time span a customer has been actively making transactions. A higher value of this metric is better than a lower one.
  • Time as Customer: the number of months between the first transaction and today. This measures how long ago a customer made the first transaction and scales the other variables. For example, a customer that made the first transaction a few months ago will necessarily have a low recency value.
  • Monetary Value: the average spend per transaction, also known as Average Order Value (AOV). This measures how much a customer spends when they make a transaction.

The great thing about summarizing everything in an RFM object is that we don’t need to keep track of specific dates and transactions. Here is what a dataset looks like in an RFM format:

While customers make transactions and stay active, and the statistical model will predict their future spending. We're taking a high-level look, but if you're curious about the distributions and academic research, check out the resources at the bottom of the page.

We fit these distributions with the RFM object and then use the estimated parameters to calculate the future spend of each customer. For each customer, we can calculate the likely number of future transactions, the probability the customer will still be active, and the average spend per transaction. Then the predicted LTV of a customer is equal to:

Expected # of Transactions x Probability of Being Active x AOV = LTV

Implementation of LTV Research

Many of our clients come to us with a goal of better understanding their customer base to make smarter marketing decisions. Through deep analysis by our Digital Intelligence team, Wpromote is able to deliver this insight and provide strategies on how to leverage across channels.

Our first step is to address the data:

  • Remove entries incorrectly formatted (e.g. characters where we should have numbers)
  • Remove all the entries with zero or negative spend (those are most likely refunds and the model can’t incorporate that)
  • Remove outliers (customers with a significant amount of transactions and/or spending a lot per transaction, as these are most likely test accounts or retailers)
  • Make sure all the remaining entries are in the correct format (for example, dates are all in the same format, spend doesn’t have $ signs that can cause it to be read as a string, and so on)

Next, we compute the RFM object. One important variable you need to decide is the time interval. In our example using planners, we decide to calculate everything in terms of months since people buy planners once or twice a year. The goal is to have a limited number of events in a single time period.

If people were buying 10 planners per month on average, the monthly time frame would not work, and we would look at weekly or daily time periods. On the other hand, if people only bought once every 3 or 4 months, you wouldn’t want to use a daily time frame because the time between transactions would be too large and this makes the estimations harder.

Key idea:  Top tier customers can be more than 10x as valuable as bottom tier customers.

After establishing the time interval, we need to make sure the model can predict the transactions and spend accurately. For this we divide the dataset into two: a training dataset and a test dataset. The training dataset is the data from 2015 and 2016, while the test dataset is the data from 2017. We use the training dataset to create the model and then to predict the transactions and spend for 2017. We then compare the predicted spend for each customer with the actual spend in 2017 to make sure the predictions were accurate.

Once we’ve passed the accuracy test, we adjust the model to fit the entire dataset and make predictions for the next 12 months. The final output for each customer contains:

  • Predicted number of transactions for the next 12 months
  • Predicted average order value per transaction
  • Total spend for the next 12 months (which is just the multiplication of the two previous metrics)
  • Cohort group, which we’ll explain in the next section

Cohort Strategy for Paid Media Teams

These groups are built based on the predicted spend for the next 12 months. Customers are generally placed into 3 different groups: high value, good value, and less valuable.

As the names suggest, the high value group contains the customers with the highest predicted spend, which is often the smallest group in number.

The less valuable group contains the customers with the lowest predicted spend. Customers in this group are predicted to have churned already.

Finally, the good value group contains the customers that have predicted spend lower than the top performers, but unlike the less valuable group, they are predicted to spend money in the next 12 months.

At this point, we have 3 distinct client lists to share with the paid media teams. They can match the customer IDs with customer emails and created audiences in AdWords and Facebook. Here are just a few of the strategies the media teams can implement using our outcome from the LTV model:

High Value Group: create lookalike audiences based on this list, and use these lookalike audiences for prospecting campaigns. Our main goal is to find new customers that look similar to the current best customers.

Good Value Group: use this list for remarketing campaigns. We can use tailored campaigns to influence these customers to spend more. We want to move them to the top tier and avoid them moving to the bottom tier.

Less Valuable Group: remove them from campaign targeting or reduce the bids. We want to save marketing dollars on customers that are predicted not to spend in the future.

Case Study

A Furniture Business: Customer Modeling Case Study

The Furniture Store wants to use customer modeling to predict the behavior of their shoppers. After gathering huge amounts of data, they apply predictive models and draw three major insights from the results:

  • Frequent purchasers skew older and have higher incomes
  • A customer who purchases more than once in six months has a much higher likelihood of purchasing again
  • Lapsed customers will buy again with the right incentives

The Furniture Store can then build their marketing strategies around these predictions.

  1. Target frequent shoppers with campaigns featuring pieces that are limited-time, interesting, or rare
  2. Offer occasional shoppers discounts and coupons to bring them into the store and encourage browsing
  3. Aim promotions at lapsed buyers (e.g. over a year or over five years) to replace items starting to show wear and tear

How might The Furniture Store implement these strategies? There are countless ways.

  • Frequent shoppers might be enticed by Instagram ads that showcase limited edition pieces.
  • Occasional shoppers could be delighted by Facebook ads that offer a 20% off code for this weekend only.
  • The lapsed buyers could be brought back into the store via a targeted email blast that encourages them to refresh the look of their home with a new rug.

This is only a taste of the possibilities that customer modeling offers.

Conclusion

Building A Predictive Future

Key takeaways:

  • Customer modeling is the practice of predicting consumer behavior using transaction data. Models determine which variables have the greatest influence on purchase predictions.
  • Prioritizing LTV allows businesses to cut spend while focusing on profit.
  • Digital Intelligence straddles the worlds of data and business to help your team build strategy and draw insights from customer modeling.

Additional Resources:

written by: Elizabeth McCumber

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